development of design guidelines for proper selection of ... · the mechanical and drainage...

STATE HIGHWAY ADMINISTRATION

RESEARCH REPORT

DEVELOPMENT OF DESIGN GUIDELINES FOR PROPER SELECTION OF GRADED AGGREGATE BASE

IN MARYLAND STATE HIGHWAYS

Ahmet H. Aydilek, Intikhab Haider, Zulkuf Kaya, Altan Cetin, and Mustafa Hatipoglu

UNIVERSITY OF MARYLAND, COLLEGE PARK

Project number SP109B4G

FINAL REPORT

January 2015

MD-15- SP109B4G-1

The contents of this report reflect the views of the authors who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Maryland State Highway Administration. This report does not constitute a standard, specification, or regulation.

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Technical Report Documentation Page 1. Report No. MD-15-SP109B4G-1

2. Government Accession No. 3. Recipient's Catalog No.

4. Title and Subtitle Development of Design Guidelines for Proper Selection of Graded Aggregate

Base in Maryland State Highways

5. Report Date January, 2015 6. Performing Organization Code

7. Author/s Ahmet H. Aydilek, Intikhab Haider, Altan Cetin, Zulkuf Kaya, and Mustafa Hatipoglu.

8. Performing Organization Report No.

9. Performing Organization Name and AddressUniversity of Maryland 1171, Glenn L. Martin Hall, University of Maryland College Park, MD-20742

10. Work Unit No. (TRAIS) 11. Contract or Grant No.

SP109B4G

12. Sponsoring Organization Name and AddressMaryland State Highway Administration Office of Policy & Research 707 North Calvert Street Baltimore MD 21202

13. Type of Report and Period Covered

Final Report

14. Sponsoring Agency Code(7120) STMD - MDOT/SHA

15. Supplementary Notes 16. Abstract The mechanical and drainage properties of graded aggregate base material are the level one input in mechanistic pavement design. Maryland State Highway Administration (SHA) is in need of guidelines for evaluation of stiffness and drainage characteristics of graded aggregate base (GAB) stone delivered at highly variable gradations to the construction sites. To fulfill the current need, the mechanical and drainage properties of several Maryland GAB materials were evaluated in the laboratory and field. The resilient modulus and hydraulic conductivity test results obtained in the laboratory were compared to the field moduli and hydraulic conductivity. The effect of moisture content on resilient modulus was also evaluated. Summary resilient modulus, SMR values at Optimum Moisture Content (OMC) minus 2% were higher than those at OMC, with few exceptions; however, the permanent deformations increased with moisture beyond OMC. The control of moisture content within 2% of OMC would not significantly affect the resilient modulus and permanent deformation in construction. It was also concluded that an addition of 4-6% fines over the SHA specification limit of 8% resulted in 2-5 times decrease in the laboratory-based GAB hydraulic conductivities and an increase in time for 50% completion of the drainage from the highway base. The required base thickness increased 2.6 to 7 times as a result of fines increased from 2 to 14% for selected GAB materials evaluated. It is recommended that resilient modulus, permanent deformation, as well as hydraulic conductivity of GAB materials, are evaluated for designing a highway base with adequate stiffness and drainage performance.

17. Key Words Graded Aggregate Base, GAB, RCA, Drainage, Hydraulic Conductivity

18. Distribution Statement: No restrictions This document is available from the Research Division upon request.

19. Security Classification (of this report) None

20. Security Classification (of this page) None

21. No. Of Pages 72

22. Price

Form DOT F 1700.7 (8-72) Reproduction of form and completed page is authorized.

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TABLE OF CONTENTS

1. INTRODUCTION ........................................................................................................ 1

2. MATERIALS ................................................................................................................ 4

3. METHODS .................................................................................................................... 8

3.1 Laboratory Geomechanical Tests ................................................................................. 8

3.2 Laboratory Hydraulic Conductivity Tests. .................................................................. 11

3.3 Field Tests. .............................................................................................................. 12

3.3.1 Light Weight Deflectometer ............................................................................... 12

3.3.2 Geogauge. ......................................................................................................... 14

3.3.3 Field Hydraulic Conductivity Tests ..................................................................... 15

4.1 CBR Tests. .............................................................................................................. 16

4.2. Resilient Modulus Tests ........................................................................................... 19

4.3. Permanent Deformation Tests ................................................................................... 27

4.4. Laboratory Hydraulic Conductivity Tests .................................................................. 29

5. PRACTICAL IMPLICATIONS .................................................................................. 46

5.1 Highway Base Design............................................................................................... 46

5.2. Effect of Hydraulic Conductivity on Highway Base Design ......................................... 47

5.3. Cost Calculations .................................................................................................... 64

6. CONCLUSIONS........................................................................................................... 65

IMPLEMENTATION PLAN AND GUIDELINES FOR PAVEMENT DESIGN. ......... 67

REFERENCES ................................................................................................................. 70

APPENDIX A ................................................................................................................... 74

APPENDIX B ................................................................................................................... 78

APPENDIX C ................................................................................................................... 85

ABBREVIATION AND ACRONYMS ........................................................................... 88

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1. INTRODUCTION Millions of tons of graded aggregate base (GAB) materials are used in construction of highway

base layers in Maryland due to their satisfactory mechanical properties. The fines content of a

GAB material is highly variable and is often related to crushing process, stockpiling in the quarry,

transportation and during construction at the site. The crushing of the stone at the quarry generally

does not decrease the mechanical strength and stiffness of the material delivered to the site.

However, the Maryland State Highway Administration (SHA) is experiencing difficulties in

achieving proper drainage through the base layers due to occasionally high fines content of the

delivered GABs. The presence of excessive water in pavement systems is one of the main causes

of pavement distress, which decreases the service life of the pavement structures significantly

(NCHRP 1997). The relatively impervious base-course materials may shorten the service life of

highways and increase the deterioration of the upper surface (asphalt layer) of pavements (NCHRP

1997).

Drainage in pavements can only be achieved with a properly designed and constructed

system that consists of all essential drainage components and a base layer with adequate

drainability and sufficient structural stability. The presence of free moisture in pavement layers has

been found responsible for many premature failures observed in both flexible and rigid pavements

(Abhijit et.al 2011). Diefenderfer et. al. (2001) present six adverse effects of excess water in

pavement life: reduction in shear strength of the unbound material, increase in differential swelling

of expansive subgrade soil, movement of fines in base and subbase layers, frost-heave and thaw

weakening, cracking in rigid pavements, and stripping of asphalt in flexible pavement. Erlingsson

et al. (2009) used heavy vehicle simulator to show that the rate of rutting depth increased in all

layers of flexible pavement structure when the groundwater table was raised. Dawson (2009) has

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also shown that poor drainage poses significant adverse effects on the condition of roadways. Free

moisture in the pavement sub layers largely occurs due to infiltration of rainwater and melted snow

through pavement surface joints or cracks. To mitigate the moisture-induced distresses, it is

imperative to drain free moisture out of pavement structures as quickly as possible via a good

drainage system. Although the performance of a subsurface drainage system depends on all of its

individual components, the hydraulic conductivity of a highway base layer can be critical for its

adequate drainage (NCHRP 1997). Several factors, including physical and chemical properties of

aggregates, geometry of pavements, climatic conditions, and pavement surface conditions, affect

the minimum hydraulic conductivity of a highway base layer or the time to achieve a certain

percentage of drainage in the pavement structure (Casagrande and Shanon, 1952).

In addition to a high quality drainage system, highway base layers should also have

satisfactory mechanical properties such as high resistance to permanent (plastic) deformation

under normal traffic loading. Therefore, it is imperative to consider structural stability in the

optimization of highway base materials. Traditionally, the California bearing ratio (CBR) test has

been used to quantify the structural stability of highway base materials due to its simplicity;

however, it does not represent the stiffness of soils at low strains. Accordingly, SHA is no longer

evaluating the pavement performance solely based on CBR test results. The resilient modulus is

arguably superior to static tests, such as CBR, due to its capability of characterizing the response

of pavement material under repeated loading that simulates traffic loading conditions (AASHTO T

307). The resilient modulus test provides an essential input parameter for the pavement design and

the permanent deformation test provides information on the rutting potential of a pavement

material in field conditions.

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There is no agreement on the minimum value of hydraulic conductivity or the time to

achieve a given percentage of drainage; however, hydraulic conductivity and the appropriate

drainage time are the indicators of pavement service life. Similarly, the minimum structural

stability required for a permeable aggregate base is not well established in the previous studies and

design guidelines. Therefore, there is a need to identify a range of gradation for highway base

materials that can provide a better characterization of structural stability along with the high

quality drainage in highway base layers.

To respond to this need, a battery of tests was conducted on graded aggregate base (GAB)

course materials, in the laboratory as well as in the field. Recycled concrete aggregates (RCAs)

and their mixtures with select GABs were also included in the laboratory testing program since

beneficial reuse of RCA brings economic advantages due to a decrease in its disposal associated

with clogging of landfill leachate collection systems. California bearing ratio (CBR), resilient

modulus, permanent deformation and hydraulic conductivity tests were conducted to investigate

the engineering properties of GAB, RCA and their mixtures, and to study the effect of curing time

on RCA. The effect of winter conditions were also evaluated by performing resilient modulus

tests on the RCA specimens after a series of freeze-thaw cycles.

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2. MATERIALS The graded aggregate base materials (GABs) included in the current testing program are

commonly used as highway base materials by the Maryland State Highway Administration (SHA).

GAB contains coarse and fine aggregate particles as well as fines (clay and silt). Generally, the

ratio between coarse and fine aggregate particles varies between 1:1 and 7:3. Seven GAB materials

were collected from different quarries in Maryland/Virginia and tested in the laboratory. GABs

used in the current study were named: A,B,C,D,E,F,G. The petrographic data shows that all

GAB materials used in this study had different mineralogy (Table 1). All GAB materials met the

SHA and AASHTO M-147 specifications and were classified as high quality base materials (A-1-a

(0) according to AASHTO Soil Classification System.

The gradations of all selected GAB materials were within the tolerance limits of SHA

specifications except few fractions of materials from the T quarry (Figure 1). The index properties

of GAB materials are shown in Table 1. All GAB materials were non-plastic and their as-received

fines content ranged from 6.9% to10%. According to SHA specifications, the fines content of the

GAB materials used in highway base layers must be less than 8% (SHA 2012). The absorption of

fine and coarse aggregates of GAB materials varied between 0.89 and 5.33% and 0.4 and 0.79%

respectively (Table 1). The Los Angeles abrasion values of all GABs were below 30%, except B-

GAB. Petrographic and mineralogical nature (marble, high CaCO3 content) of quarry ‘B’ GAB

may have caused the relatively higher loss during the abrasion tests as marble stone tends to be

easily crushed under impact loading.

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Table 1: Physical and chemical properties of the GAB and RCA materials

GA

B

Materials

Physical Properties chemical Properties

d (Pcf) OMC (%) Gs Absorption LA MD SS PD

SiO2 Al2O3 Fe2O3 CaO

Imp Vib Imp Vib F C F (%)

C (%)

% % % % % % %

A 152.3 157.2 5.80 4.70 2.55 2.77 5.33 0.78 16.40 21.90 1.60 Meta Basalt 60.88 13.27 9.43 2.93

B 152.1 154.6 4.20 4.10 2.70 2.79 1.75 0.40 53.04 24.76 0.53 Carbonate Marble 44.10 3.04 1.57 26.83

C 158.1 - 5.40 - 2.86 2.99 3.18 0.79 22.20 7.50 0.73 Basalt 38.39 9.48 7.18 5.80

D 148.8 - 5.20 - 2.75 2.79 1.32 0.58 22.90 11.50 0.60 Prasiolite 2.36 0.70 1.31 29.31

E 146.6 - 4.70 - 2.60 2.68 2.63 0.51 25.20 12.20 2.02 Carbonate-Siliceous Rock 11.90 1.95 0.85 31.67

F 157.8 162.6 5.30 4.80 2.91 3.01 0.89 0.49 26.90 18.50 2.20 Gneiss 47.71 15.61 10.95 11.90

G 154.7 157 4.80 4.50 2.72 2.83 3.09 0.55 23.60 7.56 1.10 Carbonate Dolomite 50.73 12.93 10.92 10.67

RC

GA

B

Plant A 128.4 - 9.50 - 2.29 2.49 9.23 4.20 55.20 16.80 15.70 - 51.54 4.61 2.55 16.94

Plant B 128.2 - 9.50 - 2.29 2.53 9.05 4.19 47.40 18.40 14.26 : 61.24 4.02 1.87 13.06

ϒd: maximum dry density, Imp: impact compactor, Vib: vibratory compactor, Gs: specific gravity, F: fine contents, C: Coarse contents, LA: Los Angeles abrasion test , MD: Micro deval test , SS: loss in Sodium Sulfate test, PD: Petrographic Description.

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The micro deval values of GAB materials were 7.5- 24.8%. The A and B GABs yielded high

micro deval values (21.9 and 24.8%, respectively), indicating that these GAB materials were not

durable under moist conditions. Mineralogical natures and shapes of the A and B GABs particles

could be the reason for high micro deval values. The percentage loss in sodium sulfate test for the

GAB materials ranged from 0.6 to 2.2%, meaning that all GAB materials had good resistance

against freezing and thawing process.

Two Maryland RCA materials, named A and B, were also included in the laboratory testing

program. RCAs were generated from the demolition of concrete structures and stockpiled in

Plants A and B located in Maryland. The fines content of materials A and B were measured as 6

and 9%, respectively, and grain size distribution curves of both materials were within the SHA

GAB limits (Figure 1). The absorption values of both RCAs were 4.2%; however, the Los Angeles

abrasion of A exceeded the specification limit of 50%. The percent losses based on sodium sulfate

tests were 15.7 and 14.3% for A and B, respectively, and exceeded the SHA specification limit of

12%, which could be due to a reaction of sodium sulfate with cement contents present in material.

The physical and chemical properties of the two RCA materials are summarized in Table 1.

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FIGURE 1: Gradation of (a) GAB materials, and (b) RCA materials.

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3. METHODS:

3.1 Laboratory Geomechanical Tests

The California bearing ratio (CBR) test is a penetration test for evaluation of the mechanical

behavior of road base and subbase course layers. The CBR tests were performed on A, G, F, and B

GAB materials, the two RCA materials, and their mixtures with G and A GABs. G and A GABs

were blended with two RCAs at 75:25, 50:50, and 25:75 ratios by weight. These ratios cover big

range of test results. Two types of compaction methods were utilized to observe the effect of

compaction on CBR: impact Modified Proctor compaction (ASTM D1557) and vibratory

compaction (ASTM D7382). The specimens for vibratory compaction were prepared in three

equal layers using a vibration frequency of 55 Hz for 605 seconds per layer. A BOSCH 11248

EVS model vibratory hammer was used. All specimens were compacted at their optimum

moisture contents (OMC). Table 1 provides the optimum moisture contents (OMCs) and

maximum dry unit weights (d) of the GAB and RCA materials. All CBR tests were conducted by

following the methods outlined in AASHTO T-193 and ASTM D 1883. The specimens were un-

soaked and the tests were performed at a strain rate of 0.05 in/min.

Resilient modulus test provides the stiffness of a soil under a confining stress and a

repeated axial load. The procedure outlined in AASHTO T 307-99, a protocol for testing of

highway base and subbase materials, was followed for resilient modulus tests. All specimens were

compacted by vibratory compactor in split mold of 6 inch (152 mm) in diameter and 12 inch (305

mm) in height, following the suggestions of Cetin et al. (2010). The photo of the resilient modulus

test equipment is shown in Figure B-1 (Appendix B). Resilient modulus tests were performed on

GAB, RCA and mixtures of GAB and RCA prepared at the same ratios of those tested for CBR.

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Each sample was compacted in six layers at their optimum moisture contents (OMC) and

maximum dry densities using a vibratory compactor (ASTM D73820). RCA specimens were

removed from the molds after compaction, sealed in plastic wrap, and cured at 100% relative

humidity and controlled temperature 70 3.6F (21 2 OC) for 1, 7 and 28 days before testing. In

order to evaluate the effect of moisture contents on resilient modulus (MR), specimens of all GABs

were prepared and tested at 2% below and 2% above the OMC. Resilient modulus tests were also

performed on GAB samples collected from the construction sites. The field samples were collected

from the locations where geogauge, nuclear density gauge, and light weight deflectometer (LWD)

tests were conducted. The laboratory resilient modulus tests were conducted on field-retrieved

GAB samples prepared at their field gradations, moisture contents, and compaction levels.

To determine the climate effects on the mechanical properties of RCAs, specimens were

prepared at OMC and maximum dry density in split molds and cured for 28 days before subjecting

them to 1, 4, 8, 16, and 20 cycles of freezing and thawing (F-T) per ASTM D6035. Each F-T cycle

consisted of exposing each specimen to -2.2F (-19°C) for 24 hours, followed by room temperature

(~68°F) for another 24 hours. The effect of F-T cycling on the engineering properties of recycled

materials was determined by conducting resilient modulus tests after selection of the

corresponding F-T cycles. Duplicate specimens were tested for most of the resilient modulus tests

as quality control.

A Geocomp LoadTrac-II loading frame and associated hydraulic power unit system was

used to load the specimens. The specimens were subjected to conditioning before the actual test

loading under the confining and axial stress of 15 Psi (103 kPa) for 500 repetitions. Confining

stress was kept between 3 Psi (20.7 KPa) and 20Psi (138 kPa) during loading stages, and the

deviator stress was increased from 3 Psi (20.7 kPa) to 40 Psi (276 kPa) and applied 100 repetitions

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at each step. The detailed information about the load sequences are provided in Table A-1

(Appendix A). The loading sequence, confining pressure, and data acquisition were controlled by a

personal computer equipped with RM 5.0 software. Deformation data were measured with

external linear variable displacement transducers (LVDTs) that had a measurement range of 0 to 2

inch (50.8 mm).

Resilient moduli from the last five cycles of each test sequence were averaged to obtain

resilient modulus for each load sequence. This nonlinear behavior of unbound granular material

was defined in this study using the model developed by Witczak and Uzan (1988) which

recommended the following formula:

32

31

k

a

d

k

aaR pp

pkM

(1)

where MR is resilient modulus, k1, k2, and k3 are constants, is isotropic confining pressure, d is

the deviator stress, and pa is atmospheric pressure. A summary resilient modulus (SMR) was

computed at a bulk stress of 30 Psi (208 KPa), following the guidelines provided in NCHRP 1-

28A. With few exceptions, high R2 values (R2 >0.9) were obtained from regression analyses

performed on the model.

AASHTO T-307 test guidelines were followed to run the permanent deformation tests.

During the permanent deformation test, same preconditioning load sequence of resilient modulus

tests was followed. After the preconditioning stage, the specimens were subjected to 10,000 load

repetitions under 15 Psi (103.4 kPa) confining pressure and 30 Psi (206.8 kPa) deviator stresses.

Permanent deformation tests were performed until either 10,000 load repetitions were completed

or the permanent deformation of the tested specimen was exceeded the original length of the

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specimen by 5%. A series of laboratory tests were also performed to study the effect of moisture

content (OMC, OMC-2%, and OMC+2%) on permanent deformation.

3.2 Laboratory Hydraulic Conductivity Tests.

Hydraulic conductivities of the different GAB materials were determined using a rigid-wall

permeameter that was specifically developed for testing of asphalt and GAB specimens (Kutay et

al. 2007). The GAB specimens were compacted in the mold having dimension of 8 inch (203 mm)

diameter and 8 inch (203 mm) height by using a vibratory compactor in four to six equal layers.

The test set-up allows application of a wide range of hydraulic gradients and accommodates high

flow rates that are associated with testing of permeable specimens, and significantly minimizes

sidewall leakage. The unique design also eliminates the use of valves, fittings and smaller diameter

tubing, all of which contribute to head losses that interfere with the test measurements, yet follows

all recommendations in ASTM D2434 (Figure B-2, Appendix B).

The permeameter was placed in a bath to maintain constant tail water elevation. The tub

rim was located a few millimeters above the specimen top. As the water flows out of the reservoir

tube through the specimen, air bubbles emerge from the bottom of the bubble tube. The constant

total head difference through the specimen (H) was the height difference between the bottom of

the bubble tube and the top of the water bath, and was used to calculate the hydraulic gradient (i).

The total flow rate through the specimen (Q) was determined by noting the water elevation drop in

the reservoir tube and multiplying it with the inner area of the reservoir tube (A). Finally, the

vertical hydraulic conductivities (k) were calculated using Darcy’s law.

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3.3 Field Tests.

A series of geogauge, nuclear gauge and light weight deflectometer (LWD) measurements were

conducted on the highway test sections constructed with five different GABs. The construction

sites were located at MD 200, the Inter County connector (ICC) (A GAB), I-695 (B GAB), I-295

(D GAB), MD 725 (G GAB), MD 231 (C GAB). Samples were collected from each test site

following the procedures outlined in AASHTO T-2. The field-retrieved samples were transported

to the laboratory and subjected to resilient modulus and hydraulic conductivity tests to compare

their physical and mechanical properties with those collected from the quarries. The grain size

distribution curves of the field-retrieved samples are shown in Figure 2. All gradations lie within

the SHA upper and lower gradation limits, except two samples of the T- GAB material, indicating

that the test sections were generally built by conforming to the SHA guidelines.

3.3.1 Light Weight Deflectometer Light weight deflectometer (LWD) is designed to determine the surface modulus, a response of the

underlying structure in terms of a transient deflection to the dynamic stress applied through a

circular bearing plate. Test locations at the construction site were selected on the basis of geometry

of the road. A series of density and moisture content measurements were performed via nuclear

density gauge (Figure B-3, Appendix B) at the same locations where LWD tests were executed.

The test procedure outlined in ASTM E2583 was followed to conduct the LWD tests.

Zorn® ZFG 3000 light weight deflectometer (with GPS) was used for measurements. The base

plate of the LWD equipment was placed on a flat and smooth surface, and dynamic load on the

ground was applied by dropping 22lb (10kg) load from a height of 18 inches (0.5 m). These

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measurements were at least three times at the same location and an average of the measurements

was recorded as the modulus value of the tested location.

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This deflection response is a composite response from the underlying structure within the zone of

influence, which is dictated by a combination of the plate diameter, applied dynamic load and

characteristics of the underlying materials. The zone of influence for the test may extend to a depth

equal to 1-1.5 times the plate diameter, i.e. testing undertaken with a 12 inches (300 mm) plate is

likely to have a zone of influence between 12 inches and 18 inches (300 and 450 mm) deep. The

following model was used to calculate the LWD-based modulus:

o

oo dafE 21 (2)

where Eo is surface modulus Psi , f is plate rigidity factor, v is Poisson’s ratio (~0.35), σo is

maximum contact stress Psi , a is plate radius (in), and do is maximum deflection (in).

LWD used in the study had a fixed-drop height, and deflection was measured via an

accelerometer mounted rigidly within the middle of the bearing plate. According to ASTM E2583,

the initial 1-3 drops were considered to provide a ‘seating pressure’ to ensure good contact, and

further drops were used to determine the surface modulus. A photo of the equipment is given in

Figure B-4 (Appendix B).

3.3.2 Geogauge. Geogauge was used to determine the stiffness and Young’s modulus of GAB materials at the same

locations where LWD and nuclear gauge tests were performed (Figure B-5, Appendix B).

Geogauge was placed on the ground, and slightly rotated to achieve sufficient contact between the

foot of the geogauge and ground. On hard or rough surfaces, seating of the foot was assisted by the

use of less than (10 mm) 1/4" thickness of moist sand.

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Geogauge is a hand-portable instrument that provides stiffness and material modulus

(NCHRP 10-65). The device measures the force imparted to the soil and records the resulting

surface deflection as a function of frequency. Stiffness, force over deflection, follows directly from

the impedance. Geogauge imparts very small displacements to the ground (< 1.27 x 10-6 m or <5 x

10-5 in) at 25 steady state frequencies between 100 and 196 Hz. Stiffness is determined at each

frequency and the average stiffness for the 25 frequencies is displayed in lb/in. The entire process

takes about one minute. At these low frequencies, the impedance at the surface is stiffness-

controlled and is proportional to the shear modulus of the soil. The stiffness, K (lb/in), is

calculated using the following equation:

)1(

77.12

REP

K (3)

where P (lb) is load, δ (inch) is deflection, R (in) is radius of the contact ring, E (lb/in2) is shear

modulus and υ is Poisson ratio.

3.3.3 Field Hydraulic Conductivity Tests

A series of borehole hydraulic conductivity tests were conducted at the construction sites,

following the procedure outlined in ASTM D6391 (Figure B-6, Appendix B). The first stage of

the test method was employed as it provides the vertical hydraulic conductivity. Bentonite was

used as a sealant around the borehole, and tests were performed on the basis of falling head

method. Furthermore, GAB samples were collected from each test site and compacted to field

density and water content upon transporting to laboratory. A series of laboratory hydraulic

conductivity tests were performed on the field-retrieved samples by following the procedures

outlined in Section 3.2.

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4. RESULTS AND ANALYSIS

4.1 CBR Tests.

Table 2 shows the CBR results for GAB materials. The CBR of BLGAB was the highest (218)

among others while the R- GAB resulted in the lowest CBR (68). The reason for such variation in

CBR values of GAB materials could be different gradations, packing arrangement of particles and

fines content. It is well-known that the structural stability of an unbound aggregate is affected by

its particle size distribution (gradation), particle shape, packing arrangements, and angularity of the

coarse particles (White et al. 2002). The CBRs of all GABs prepared with impact compaction

method are significantly lower than those prepared with the vibratory compaction. Fines content

increased due to crushing of the coarse aggregate during the impact compaction process as shown

in Figure A-1 (Appendix A). Siswosoebrotho et al. (2005) showed that coarse materials contained

more than 4% fines content decreased the CBR value since excessive fines caused reduction in

interlocking between the angular aggregates, which may have influenced the strength of the coarse

material. The data by Bennert and Maher (2005) also revealed that an increase in fines content

decreases the CBR values significantly, consistent with the findings obtained in the current study.

Therefore, all GABs were compacted by a vibratory hammer before performing resilient modulus,

permanent deformation, and hydraulic conductivity tests.

Table 3a shows that the RCA specimens cured for 1 day resulted in lower CBR than those

subjected to 7 day-curing. Poon et al. (2006) stated that unhydrated cement content retained

within the adhered mortar was the cause of self-cementing in RCA used as unbound base. Table 3b

presents the results of CBR tests performed on mixtures of RCA and GABs.

17

Table 2: CBR, SMR, power fitting parameters and plastic strain of the GAB materials

GAB Material

CBR Average SMR (Psi) Mean power model fitting parameters

(Standard deviation) ϵ Plastic strain (%) Impact

Compt. Vibratory

compaction

Field- retriev

ed

Lab OMC-2

(%)

Lab OMC (%)

Lab OMC+

2 (%)

k1 (σ) k2(σ) k3(σ)

A 58 68 21464 27991 26105 21900 1025 (216)

0.88 (.09)

-0.22 (0.07)

0.035

B 85 150 23495 25525 37273 21464 803.9 (310)

1.08 (0.28)

-0.15(0.13)

0.03

C NA NA 23205 18274 14648 17259 663.5 (183)

0.96 (0.27)

-0.15(0.08)

0.07

D NA NA 32197 18854 17404 17984 894.4 (118)

0.72 (0.09)

-0.11(0.09)

0.045

E NA NA NT 21319 19434 12618 666.6 (139)

0.96 (0.12)

-0.10(0.02)

0.09

F 175 200 NT 20304 12618 NT 679.9 (278)

1.07 (0.25)

-0.16(0.06)

0.06

G 121 218 19579 18854 16533 17259 1121 (606)

0.91 (0.33)

-0.16(0.09)

0.04

Notes: CBR: California bearing ratio, SMR: summary resilient modulus, OMC: optimum moisture content, ϵplastic: plastic strain of specimens after 10,000 repeated load cycles. NA: Not analyzed.

18

Table 3a. Effect of curing time and freeze-thaw cycles on CBR and SMR of the two RCAs.

RCA

CBR SMR (psi) Mean power model fitting parameters

(Standard deviation) Curing Time

Freezing and Thawing Cycles

1 day

7 days

0 4 8 12 14 20 k1 k2 k3

A 148 167 14793 16533 14503 NA 20304 31181 355.8 (17.2)

1.40(0.09)

-0.18(0.09)

B 114 131 17694 17694 17839 18274 NA 18854 493.3 (35.2)

1.18(0.07)

-0.13(0.07)

Table 3b. CBR, SMR and power model fitting parameters of RCA/GAB mixtures.

RCA/GAB mixtures

CBR SMR (psi)

Power model fitting parameters

k1 k2 k3

25A75G 282 20304 1495 0.80 -0.04 50A50G 319 21755 543.5 1.30 -0.10 75A25G 301 37708 492.3 1.29 -0.11 25A75A 209 23205 1430 0.82 -0.20 50A50A 131 18854 356.5 1.54 -0.17 75A25A 154 40608 478.1 1.45 -0.34 25B75G NA 49310 452.3 1.36 -0.05 50B50G NA 40608 1689 0.68 -0.08 75B25G NA 17404 2313 0.53 -0.18 25B75A 141 10152 510.61 1.29 -0.18 50B50A 194 17404 450.63 1.27 -0.11 75B25A 189 21755 356.25 1.39 -0.21

Notes: A: RCA from Plant A, B: RCA from Plant B, NA: Not analyzed,

19

BL-based mixtures resulted in higher CBRs but a consistent trend cannot be observed with CBR

value and percent RCA addition.

4.2. Resilient Modulus Tests

Average SMR of GAB materials collected from construction sites and quarry locations are shown

in Table 2. The difference in SMR of quarry and field-collected samples may be due to a change in

gradation, moisture content, fines content, and unit weight. Several studies suggest that the

resilient modulus generally increases with an increase in density of the tested material (Robinson

1974, Rada and Witczak 1981, Kolisoja 1997). The number of contacts per particle increases

significantly with increased density resulting from additional compaction of the particulate system.

This, in turn, decreases the average contact stress corresponding to a certain external load. Hence,

the deformation in particle contacts decreases and the resilient modulus increases (Kolisoja, 1997).

Figure 3 shows that resilient modulus of GAB increases considerably with an increase in

bulk stress, consistent with the findings of previous studies (Hicks 1970, Smith and Nair 1973,

Uzan 1985, and Sweere 1990). Table 2 and Figure 4 show that the resilient moduli of the GAB

materials prepared at OMC-2% were generally higher than the moduli of those prepared at

OMC+2%, except B-GAB. These findings were consistent with the previous studies. For instance,

Smith and Nair (1973) and Vuong (1992) indicated that the resilient response of dry and partially

saturated granular materials was high, but as complete saturation was approached, the resilient

behavior of these materials had been affected significantly. Past research also revealed that the

resilient moduli of granular materials were highly dependent on the moisture levels and tended to

decrease near saturation (Haynes and Yoder 1963, Hicks and Monismith 1971). Furthermore,

20

Dawson et al. (1996) studied a range of well-graded unbound aggregates and determined that,

below the

0

20000

40000

60000

80000

100000

0 20 40 60 80 100 120 140 160

G

F

C

D

E

A

B

y = 87.801 + 595.94x R2= 0.89895

y = -705.54 + 600.36x R2= 0.95716

y = 9620.4 + 388.76x R2= 0.95887

y = 12674 + 377.62x R2= 0.92609

y = 3088.6 + 496.15x R2= 0.97694

y = 13142 + 706.3x R2= 0.83176

y = 5585.3 + 920.28x R2= 0.89961

Res

ilien

t m

od

ulu

s (P

si)

Bulk stress (Psi)

FIGURE 3: GAB resilient moduli at different loading sequences

21

10000

15000

20000

25000

30000

35000

40000

0 2 4 6 8 10

A

B

G

E

D

C

F

SM

R (P

si)

Moisture Content (%)

FIGURE 4: Effect of moisture content on SMR values of GABs.

22

optimum moisture content, stiffness tended to increase apparently due to development of suction.

Beyond the optimum moisture content, as the material became more saturated and excess pore

water pressure was developed, the trend was shifted and stiffness started to decline rapidly.

An exception to the observed trend in Figure 4 was with the B-GAB material, which

experienced a maximum SMR when compacted at its optimum moisture content. Thom and Brown

(1987) observed a similar behavior in testing of select aggregates and attributed it to lubricating

effect of moisture on particles that would decrease the deformation of the aggregate assembly and

yield a resilient modulus increase.

Figure 5 shows the effect of fines content and gravel/sand ratio on the resilient moduli of

A-GAB material, respectively. SMR increased 2-2.5 times with an increase in fines content from 2

to 8% by weight, and then gradually decreased with further addition of fines (Figure 5a). A nearly

bell-shaped relationship can be observed when the gravel-to-sand ratio is plotted against SMr

(Figure 5b). SMr increases nearly 1.4 times with a change in G/S ratio from 1.5 to 1.7, and reaches

its maximum value at the optimum G/S value of ~1.7. Further decrease in sand fractions makes

the material unstable depending on the gravel size distribution. Similar observations were made

by Xiao et al. (2009) on testing three aggregates with different petrography. Previous studies

reported that the resilient modulus of granular materials generally tended to decrease with an

increase in fines content (Thom and Brown 1987; Kamal et al. 1993). Jorenby and Hicks (1986)

also showed that initially an increase in fines content provided higher stiffness in granular

materials and then a considerable reduction in stiffness was observed as more fines content were

added to a crushed aggregate.

The trends in Figure 5 can be explained by the influence of fines addition to packing of the

particles in a soil matrix. Figure 6 shows the hypothetical packing arrangements of coarse

23

0

5000

10000

15000

20000

25000

30000

0 3 6 9 12 15

SM

R (

Psi

)

Fines content (%)

Y = M0 + M1*x + ... M8*x8 + M9*x9

-1129.6M0

8555.7M1

-915.88M2

29.212M3

0.62776R2

(a)

5000

10000

15000

20000

25000

30000

35000

40000

1.4 1.5 1.6 1.7 1.8 1.9

SM

R (

Ps

i)

Gravel/Sand Ratio

Y = M0 + M1*x + ... M8*x8 + M9*x9

4.7669e+6M0

-8.9467e+6M1

5.6008e+6M2

-1.1643e+6M3

0.61876R2

(b)

FIGURE 5: Effect of (a) fines content, and (b) gravel-to-sand ratio on SMR of A GAB.

24

Large G/S Optimum G/S Small G/S

(a) (b) (c) FIGURE 6: Arrangement of particles in a soil matrix with the variation of fines. (a) No or small fines content (large G/S ratio), (b) dense graded (optimum G/S), and (c) high fines content (small G/S) (After Yoder and Witczak 1975, and Xiao et al. 2012)

25

Particles with varying fine contents in a soil matrix. Coarse aggregates can be interlocked with

each other yielding lower density and large voids due to lack of fines, as shown in Figure 6a. This

kind of soil matrix is referred to as gap graded gradation, and brings several advantages. The

matrix provides good drainage and is less susceptible to frost and heave processes. Xiao et al.

(2012) claimed that the GABs at this state may develop an unstable permanent deformation

behavior. The soil matrix shown in Figure 6b is classified as dense-graded in which most of the

voids between the aggregates are filled with fines but coarse particles are still in contact with each

other. The grain-to-grain contact and void filling with fines are the possible reasons for the

strength gain in this state. This soil matrix provides higher density which yields higher stiffness yet

decreases the hydraulic conductivity. On the other hand, the compaction of such a soil matrix is

moderately difficult. There is no grain-to-grain contact of aggregates in a soil matrix shown in

Figure 6c. It has reasonably low density and hydraulic conductivity and the coarse particles are

floating in the fine particles. The compaction of this kind of soil matrix is easier; however, its

stability can easily be affected by adverse water conditions.

The initial improvement in stiffness observed in the current study was attributed to

increased contacts during pore filling as explained (Figure 6b). Addition of excessive fines

gradually displaced the coarse particles in the soil matrix and caused stiffness to decrease (Figure

6c). The initial increase in resilient modulus with an increase in fines content was due to packing

of aggregates which decreased the recoverable strain of the material and resulted in stiff material

(Figure 5b).

Resilient modulus tests were also performed on RCAs and mixtures prepared at varying

RCA-to-GAB ratios. It can be seen from Figure 7 and Table 3b that 100%RCA and 100%GAB

provide relatively higher MR values as compared to their mixtures, with few exceptions. Similar

26

0

2 0 0 0 0

4 0 0 0 0

6 0 0 0 0

8 0 0 0 0

1 0 0 0 0 0

1 2 0 0 0 0

0 2 0 4 0 6 0 8 0 1 0 0

B G (7 5 -2 5 ) B G (5 0 -5 0 ) B G (2 5 -7 5 )R C G A B 'B 'G -G A B

Re

silie

nt

mo

du

lus

(Psi

)

B u lk s tre s s (P s i)

0

2 0 0 0 0

4 0 0 0 0

6 0 0 0 0

8 0 0 0 0

1 0 0 0 0 0

0 2 0 4 0 6 0 8 0 1 0 0

R C G A B 'B 'B A (7 5 -2 5 )B A (5 0 -5 0 ) B A (2 5 -7 5 )A -G A B

Res

ilie

nt

mo

du

lus

(Psi

)

B u lk s t re s s (P s i )

0

2 0000

4 0000

6 0000

8 0000

1 0 000 0

0 2 0 4 0 60 80 1 00

A G (75 -25 )A G (50 -50 )A G (25 -75 )R C G A B 'A 'G -G A B

Res

ilie

nt

mo

du

lus

(Psi

)

B u lk s tress ( P s i)

0

2 0 0 00

4 0 0 00

6 0 0 00

8 0 0 00

1 0 0 00 0

0 2 0 4 0 6 0 8 0 1 0 0

R C G A B 'A 'A A (7 5 -2 5 )A A (5 0 -5 0 )A A (2 5 -7 5 )A -G A B

Res

ilie

nt

mo

du

lus

(Psi

)

B u lk s tre s s (P s i)

FIGURE 7: Resilient moduli of recycled aggregate A and B, and their mixtures with two GABs.

27

observations were made by Kazmee et al. (2012) who attributed this behavior to poor packing of

particles and change in gradation parameters. Table 3a indicates that the SMR of RCAs tend to

increase with an increase in freezing and thawing cycles. The trends are reported by Bozyurt et.al

(2011). The stiffness increase in the current study is attributed to the continuation of hydration

(cementation) reactions in RCA during the freeze-thaw cycles.

4.3. Permanent Deformation Tests

Granular materials exhibit permanent deformation if they are subjected to repetitive loading for

extended periods of time. The permanent deformation values are strongly dependent on rigidity,

shear stress and load capacity of the granular materials. Use of the resilient modulus by itself is not

sufficient to fully characterize the mechanical behavior of a pavement structure and should be

coupled with permanent deformation tests (Khogali and Mohammad, 2004).

Figure 8 shows the variation of cumulative permanent axial strain (plastic strain) with

applied number of load repetitions. To model the relationship between the applied number of load

repetitions and plastic strain, a power model was used:

baNP (4)

where a, b are fitting parameters; P is the cumulative permanent axial strain and N is the number

load repetitions. The permanent deformation (i.e., plastic strain) depends on the packing

arrangement of particles, grain size distribution, and particle contact area. Table 2 shows the

plastic strain of all GAB materials used in the current study. The E-GAB had the maximum plastic

strain (0.09%) while T- GAB had the minimum plastic strain (0.03%) among all GAB materials

after 10,000 repeated cycles of loading, which may be attributed to the gravel contents of

28

0

0.02

0.04

0.06

0.08

0.1

0 2000 4000 6000 8000 10000

G F C D E A B

y = 0.0057315 * x^(0.22031) R2= 0.96927

y = 0.010909 * x^(0.20494) R2= 0.95451

y = 0.0081745 * x^(0.24811) R2= 0.96024

y = 0.011369 * x^(0.17083) R2= 0.93148

y = 0.0099292 * x^(0.24348) R2= 0.97282

y = 0.0088956 * x^(0.16364) R2= 0.94868

y = 0.0058739 * x^(0.18826) R2= 0.88511

Pla

sti

c s

trai

n (

%)

Number of repeated load cycles

FIGURE 8: Plastic strain of GABs under repeated load cycles. All specimens are compacted at OMC.

29

the two GABs. The K- GAB matrix has higher gravel content (56% versus 46%) and thus includes

more voids (Figure 6a). Such a matrix, due to lack of good amount of fines and sand, includes

gravel-to-gravel contact only and may experience more deformation during repeated loading (Xiao

et al. 2012).

The data in Figure 9 suggest that GABs compacted wet of optimum are more susceptible to

structural rutting. Khogali et al. (2004) also observed 2-3 times increase in permanent

deformations of roadway bases due to fluctuations in groundwater table. Uthus (2007) reported

that dry density, degree of saturation, and stress level seemed to be the key parameters for

influencing the permanent deformation behavior, along with mineralogy, fines content and grain

size distributions of the granular materials. Increase in moisture content from OMC-2% to OMC

for all GABs yielded approximately the same amount of plastic strain under long term repetitive

loads (Figure 9). The addition of moisture above OMC caused pore water pressure increase under

repeated loading and resulted in excessive deformations.

As shown in Figure10, the permanent deformation of GAB increases upon mixing with

RCA, suggesting higher likelihood of rutting of a pavement system built with GAB/RCA blends.

Similar observations were made by Kazmee et al (2011). It is also noted that the plastic strain in

individual GAB and RCA materials is less than their mixtures. This could be due to poor packing

arrangement of particles when these two materials are mixed.

4.4. Laboratory Hydraulic Conductivity Tests

Table 4 summarizes the hydraulic conductivities of seven GABs tested in the laboratory and in-

situ. In-situ hydraulic conductivities are only 0.76-1.64 and 0.6-1.48 times higher than the

laboratory-measured hydraulic conductivities of the samples collected from the quarries and field

30

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0 2000 4000 6000 8000 10000

OMCOMC-2%OMC+2%

Pla

stic

str

ain

(%

)


0

0.02

0.04

0.06

0.08

0.1

0 2000 4000 6000 8000 1000

OMCOMC-2%OMC+2%

Pla

stic

str

ain

(%

)Number of repeated load cycles

0

0.02

0.04

0.06

0.08

0.1

0.12

0 2000 4000 6000 8000 10000

OMCOMC-2%OMC+2%

Pla

sti

c st

rain

(%

)


0

0.02

0.04

0.06

0.08

0.1

0.12

0 2000 4000 6000 8000 1000

OMCOMC-2%OMC+2%

Pla

sti

c s

trai

n (

%)


FIGURE 9: Effect of moisture content on plastic strain of the GABs (OMC=optimum compaction moisture content).

A-GAB B-GAB

F-GAB C-GAB

31

0

0.2

0.4

0.6

0.8

1

0 2000 4000 6000 8000 10000

A25-A75A50-A50A75-A25RCGAB 'A'A-GAB

Pla

stic

str

ain

(%

)


RCA-Plant A

0

0.1

0.2

0.3

0.4

0 200 400 600 800 1000

A25-B75A50-B50A75-B25RCGAB 'B'A-GABI

Pla

stic

str

ain

(%

)


RCA-Plant B

FIGURE 10: Plastic strain of recycled concrete aggregates A and B, and their mixtures with A- GAB

32

Table 4: Mean hydraulic conductivity of GAB materials tested in the laboratory and in-situ.

GAB Material k laboratory of quarry

samples, ft/day (cm/s)

k laboratory of field-retrieved samples

(ft/day)

k in-situ

(ft/day)

A 77

(2.73 x 10-2) 71

(2.52 x 10-2) 59

(2.07 x 10-2)

B 19

(6.57 x 10-3) 20

(7.23 x 10-3) 20

(7.05 x 10-3)

C 2

(5.66 x 10-4) 2

(6.30 x 10-4) 3

(9.30 x 10-4)

D 42

(1.48 x 10-2) 20

(6.90 x 10-3) 12

(4.25 x 10-3)

E 11

(3.92 x 10-3) NA NA

F 4

(1.48 x 10-3) NA NA

G 36

(1.28 x 10-2) 37

(1.30 x 10-2) 18

(6.20 x 10-3) NA: Not analyzed

33

test locations, respectively. The difference is less than an order of magnitude, suggesting that the

laboratory and field hydraulic conductivities are comparable.

In order to study the effect of fines content on hydraulic conductivity, gradations of A- and

B-GAB materials were adjusted by following two different approaches. First, gradation was

adjusted between the US. No #4 and #30 sieves and the rest of the fractions were kept constant

(Figure 11a). Figure 12a shows that such an adjustment does not significantly alter hydraulic

conductivity of R- GAB. These results confirm the commonly observed trend that the coarser

portion of sand in GAB (e.g., between the U.S. #4 and #30 sieves) does not have a significant

effect on hydraulic conductivity and flow is mainly controlled by smaller particles in the gradation

(Cote and konrad 2003). Therefore, at the second stage, adjustments were made for the fractions

between the 3/8-inch (9.5-mm) and 1/2-inch, (12.7-mm) and 3/4-inch (19-mm) sieves to

represent a more widespread change in grain size distribution (Figures 11b and 11c). The data in

Table 5 reveal that A- and B-GABs hydraulic conductivities reduced 5 and 50 times, respectively,

as a result of such fines content adjustment from 2 to 16% (Figures 12b and 12c). Similar

observations were reported by Siswosoebrotho et. al. (2005) during testing of unbound granular

materials.

Figure 13 shows that hydraulic conductivities of A- and B-GAB materials increase up to 5

and 50 times, respectively, with nearly 14% increase in gravel content for both materials. Similar

magnitudes of increase in hydraulic conductivity were observed when gravel-to-sand (G/S) ratio

was varied between 1.45 and 1.85 R-GAB and 0.82 and 1.14 for T-GAB (Figure 14). Analysis of

the trends in Figure 14 shows that the soil matrix is porous and leads to higher hydraulic

conductivities when G/S ratio >1.7 for R and G/S>1.05 for B GABs. It is believed that the

minimum porosities are achieved at these gravel-

34

0

10

20

30

40

50

60

70

80

90

100

0.010.1110100

Original quarry2% fines6% fines8% fines10% fines12% fines14% finesSHA Upper Limit SHA Lower Limit

Per

cen

t fi

ner

Particle size (mm)

#200

#30

3/4

1 1/

2

1/2

3/8 #4

A- GAB

(a)

0

10

20

30

40

50

60

70

80

90

100

0.010.1110100

Original quarry2% fines4% fines6% fines8% fines10% fines12% fines14% fines16% finesSHA Upper Limit SHA Lower Limit

Per

cen

t fi

ner

Particle size (mm)

#20

0

#3

0

3/4

1 1

/2

1/2

3/8 #4

A- GAB

(b)

0

10

20

30

40

50

60

70

80

90

100

0.010.1110100

Original quarry2% fines4% fines6% fines8% fines10% fines12% fines14% fines16% finesSHA Upper Limit SHA Lower Limit

Pe

rcen

t fi

ner

Particle size (mm)

#200

#3

0

3/4

1 1/

2

1/2

3/8 #4

B-GAB

(c)

FIGURE 11: Change in gradations of (a) A- GAB due to adjustment between #30 to #4 sieves, and (b) A and (c) B GABs due to adjustment between 3/4in to #4 sieves.

35

10

100

1000

0 2 4 6 8 10 12 14 16

Hyd

rau

lic

co

nd

uct

ivit

y (

ft/d

ay)

Fines Content (%)

(a)

A- GAB

1

10

100

1000

0 2 4 6 8 10 12 14 16

Hy

dra

uli

c co

nd

uct

ivit

y (f

t/d

ay)

Fines content (%)

SHA upper limit for gradation

SHA lower limit for gradation

A- GAB

(b)

Y = M0 + M1*x + ... M8*x8 + M9*x9

102.64M0

-11.351M1

0.38988M2

0.91896R2

0.1

1

10

100

0 2 4 6 8 10 12 14 16

y = 89.928 * x (-2.1883) R2= 0.73377

Hyd

rau

lic

con

du

cti

vity

(ft

/day

)

Fines content (%)

SHA upper limit for gradation

SHA lower limit for gradation

B- GAB

(c)

FIGURE 12: Effect of fines on hydraulic conductivity of (a) A GAB due to adjustment between #30 and #4 (0.6 mm and 4.75 mm sieves), and (b) A and (c) B GABs due to adjustment between 3/4in to #4 sieves.

36

10

20

30

40

50

60

70

80

90

45 50 55 60 65 70

y = 0.081814 * e (0.10709x) R2= 0.90999 H

ydra

uli

c co

nd

uct

ivit

y (f

t/d

ay)

Gravel content (%)

(a)

5

10

15

35 40 45 50 55 60

y = 5.3859e-7 * e (0.32115x) R2= 0.94017

Hy

dra

uli

c co

nd

uct

ivit

y (f

t/d

ay)

Gravel content (%)

(b)

FIGURE13: Effect of gravel content on hydraulic conductivity of

(a) A and (b) B GABs

37

Table 5: Effect of gradation parameters on SMR and hydraulic conductivity of GAB materials.

Material FC D10 D30 D60 D50 Sand Gravel G/S SMR k

(%) in in in in (%) (%) Ratio Psi cm/s ft/day

A- GAB Change in

Fines content is adjusted in sand portion of gradation

2 0.007 0.084 0.404 0.280 34.4 63.6 1.17 - 1.01

x 10-2 29

6 0.009 0.067 0.394 0.256 34.4 59.6 1.27 - 2.56

x 10-2 73

8 0.008 0.071 0.394 0.256 34.4 57.6 1.33 - 3.85

x 10-2 109

10 0.006 0.067 0.394 0.256 34.4 55.6 1.38 - 3.40

x 10-2 96

12 0.004 0.079 0.394 0.256 34.4 53.6 1.44 - 6.33

x 10-2 179

14 0.004 0.079 0.394 0.256 34.4 51.6 1.56 - 3.83

x 10-2 109

A- GAB Change in

fines content is adjusted in

coarse portion of gradation

2 0.016 0.106 0.421 0.307 34.4 63.6 1.85 12502 3.12

x 10-2 88

4 0.009 0.087 0.409 0.280 34.4 61.6 1.79 21493 1.92

x 10-2 54

6 0.006 0.075 0.394 0.268 34.4 59.6 1.73 18883 1.83

x 10-2 52

8 0.005 0.067 0.366 0.244 34.4 57.6 1.67 26076 1.04

x 10-2 29

10 0.003 0.051 0.354 0.228 34.4 55.6 1.62 18375 1.11

x 10-2 31

12 0.002 0.045 0.323 0.209 34.4 53.6 1.56 21247 1.14

x 10-2 32

14 0.001 0.038 0.307 0.199 34.4 51.6 1.5 19231 6.33

x 10-3 18

16 0.001 0.032 0.280 0.169 34.4 49.6 1.44 - 6.40

x 10-3 18

B- GAB, Change in

fines content is adjusted in

coarse portion of gradation

2 0.006 0.035 0.354 0.205 45.8 52.2 1.14 - 3.67

x 10-3 10

4 0.005 0.030 0.323 0.193 45.8 50.2 1.1 - 2.71

x 10-3 8

6 0.004 0.024 0.295 0.157 45.8 48.2 1.05 - 1.17

x 10-3 3

8 0.004 0.020 0.276 0.126 45.8 46.2 1.01 - 4.22

x 10-4 1

10 0.003 0.017 0.236 0.106 45.8 44.2 0.97 - 4.60

x 10-4 1

12 0.002 0.014 0.213 0.091 45.8 42.2 0.92 - 6.44

x 10-5 0.2

14 0.002 0.012 0.185 0.075 45.8 40.2 0.88 - 7 x 10-5

0.2

16 0.002 0.010 0.157 0.063 45.8 38.2 0.83 - 7.32

x 10-5 0.2

A- GAB QG 7.6 0.005 0.071 0.394 0.256 34.4 58.03 1.69 190 2.73

x 10-2 77

B GAB QG 8.6 0.005 0.020 0.276 0.126 45.8 45.6 1 257 6.57 x 10-3

19

38

SHA Spec. LL

0 0.016 0.118 0.472 0.374 36 64 1.78 - 3.19

x 10-2 90

SHA.Spec UL

8 0.004 0.033 0.236 0.126 48 44 0.92 - 3.84

x 10-3 11

FC: Fines contents, D10,D30,D50,D60 : Diameter of particles @ 10,30,50,60 passing percentage finer respectively, G/S: Gravel to Sand ratio, SMR: Summary Resilient Modulus, k : hydraulic conductivity, QG: quarry gradation, LL: Lower limit of SHA specified Gradation, UL: Upper Limit of SHA specified gradation

39

10

20

30

40

50

60

70

80

90

1.4 1.5 1.6 1.7 1.8 1.9

Hyd

rau

lic

con

du

ctiv

ity

(ft/

day

)

Gravel/sand ratio

(a)

Y = M0 + M1*x + ... M8*x8 + M9*x9

1006.2M0

-1333.7M1

450.88M2

0.92424R2

3.75

7.5

11.2

15

0.8 0.85 0.9 0.95 1 1.05 1.1 1.15

y = 6.0696e-7 * e (14.579x) R2= 0.95304

Hyd

rau

lic

con

du

cti

vit

y (

ft/d

ay)

Gravel/sand ratio

(b)

FIGURE14: Effect of gravel/sand ratio on hydraulic conductivity of

(a) A and (b) B GABs

40

to-sand ratios due to optimum packing of the GAB medium. Xiao et al. (2012) also reported

minimum porosity achievements at G/S=1.56-1.68 and G/S~1.5, respectively, for GABs with

varying petrography.

Figure 15 shows that hydraulic conductivites of R and B GABs increased up to 5 and 50

times, respectively, with increasing characeterictic grain sizes of the soil (i.e., D10, D30 , D50 and

D60). The hydraulic conductivity seems to be more sensitive to the smaller grain sizes (D10 and

D30) as compared to larges sizes (D50 and D60), consistent with the previous studies that finer sizes

play a major role in hydraulic conductivity changes (FHWA 2005).

41

23.7

47.5

71.2

95

0 0.1 0.2 0.3 0.4 0.5

D10

D30

D60

D50

y = -104.47 + 405.67x R 2= 0.7573 y = -71.456 + 469.35x R 2= 0.83425

y = 16.216 + 4471.5x R 2= 0.93783

y = -13.556 + 859.18x R 2= 0.88294 H

ydra

uli

c co

nd

uc

tivi

ty (

ft/d

ay)

Particle diameter, D (in)

A- GAB

(a)

0

5

10

15

0 0.1 0.2 0.3 0.4 0.5

D30

D10

D50

D60

y = -5.4632 + 415.47x R2= 0.89907

y = -5.275 + 2350x R2= 0.83754

y = -5.647 + 67.693x R2= 0.86339

y = -9.5027 + 48.858x R2= 0.74882

Hy

dra

uli

c c

on

du

ctiv

ity

(ft

/day

)

Particle diameter, D (in)

B- GAB

(b)

FIGURE 15: Effect of grain sizes on hydraulic conductivity of (a) A and (b) B GABs.

42

4.5 Field Tests

The in-situ stiffness and modulus values of the GAB materials were measured via light weight

deflectometer (LWD) and geogauge, and the data are summarized in Table C1 of Appendix C. The

field stiffness and moduli of the GAB materials are plotted against laboratory determined SMR in

Figure 16. In order to determine the correlation between the laboratory resilient moduli and the

moduli/stiffness obtained from geogauge and LWD, a paired t-test was conducted for statistical

significance by determining whether the Pearson correlation coefficient between laboratory and

field resilient modulus/stiffness is statistically different from zero. For this statistical analysis, the

t-statistic (t) was computed from the correlation coefficient (r) as:

2

1 2

n

r

rt

(5)

where ρ is the population correlation coefficient (assumed to be zero) and n is the number of

degrees of freedom. n was equal to 54, 5, and 5 for geogauge versus LWD test data, geogauge

versus laboratory SMR data, and LWD versus laboratory SMR data, respectively. A comparison

was made between t and the critical t (tcr) corresponding to a significance level, α. If t > tcr, then

the Pearson correlation coefficient was significantly different from zero and a significant

relationship was assumed to exist between laboratory and field resilient modulus. In this analysis,

was set to 0.05 (the commonly accepted significance level), which corresponds to tcr = 2.011 for

geogauge versus LWD data and tcr = 3.182 for geogauge versus laboratory SMR, and LWD versus

laboratory SMR data.

Figure 16 shows that the correlation between geogauge and LWD is high (t=9.6>tcr=2.011).

The coefficient of determination, R2, for the correlation between the data produced by the two field

43

0

5000

10000

15000

20000

25000

30000

35000

0 10000 20000 30000 40000 50000

y = 0.42795x

LW

D m

od

ulu

s (P

si)

Geogauge Young's modulus (Psi)

r = 0.81t = 9.6 > t

cr = 2.011

0

5000

10000

15000

20000

25000

30000

35000

0 50000 100000 150000 200000 250000

y = 0.094929x

LW

D m

od

ulu

s (P

si)

Geogauge stiffness (lb/in)

r = 0.81t = 9.6 > t

cr = 2.011

80000

90000

100000

110000

120000

130000

140000

150000

20000 22000 24000 26000 28000 30000 32000

y = 4.5302x

Mea

n g

eog

aug

e st

iffn

ess

(lb

/in

)

Mean SMR(Psi)

r = 0.95t = 5.3 > t

cr = 3.182

18000

20000

22000

24000

26000

28000

30000

32000

20000 22000 24000 26000 28000 30000 32000

y = 0.98693x

Mea

n g

eo

ga

ug

e Y

ou

ng

's m

od

ulu

s (

Ps

i)

M ean SMR

(Psi)

r = 0.91t = 3.8 > t

cr = 3.182

6 0 0 0

8 0 0 0

1 0 0 0 0

1 2 0 0 0

1 4 0 0 0

1 6 0 0 0

1 8 0 0 0

6 0 0 0 8 0 0 0 1 0 0 0 0 1 2 0 0 0 1 4 0 0 0 1 6 0 0 0 1 8 0 0 0 2 0 0 0 0

y = 0 .7 7 9 9 3 x

Mea

n L

WD

mo

du

lus

(P

si)

M e a n MR

@ L W D B u lk S t r e s s (P s i)

B - G A B

A -G A B

D -G A B

C -G A B

G -G A B

r = 0 .9 7t = 6 .9 > t

c r = 3 .1 8 2

FIGURE 16: Comparison of laboratory and field moduli.

44

equipment was fair (R2>0.65). The differences in induced stress and depth of influence of the

applied load provided by LWD, and geogauge could be the possible reasons for the observed

correlation. Previous studies showed that applied stress level was the factor that had the most

significant impact on the resilient properties of granular materials (Kolisoja (1997). Significantly

higher R2 values were observed for the correlations between the mean laboratory SMR and LWD

or geogauge data (R2=0.83-0.97). In addition, t values that were obtained from statistical analyses

(t>tcr= 3.182) indicate that reasonably good correlations exist between the geogauge and laboratory

SMR as well as LWD and laboratory MR data at LWD bulk stress level. All regression lines were

forced to pass through the zero intercept because of rationality of relations.

Figure 17 shows the field and laboratory hydraulic conductivity test results. The

differences in the laboratory and field hydraulic conductivity values are negligible considering the

anisotropy in the field. The drainage qualities of the GAB materials tested in the laboratory and

field can be considered as “fair to good” according to the hydraulic conductivity range provided in

AASHTO Guide (1993).

45

10

20

30

40

50

60

70

80

D-GAB A-GAB C-GAB G-GAB B-GAB

Laboratory test--Quarry samplesLaboratory test--Field retrieved samplesField test

Hy

dra

uli

c c

on

du

cti

vit

y (f

t/d

ay)

FIGURE 17: Comparison of laboratory and field hydraulic conductivities of GAB materials.

46

5. PRACTICAL IMPLICATIONS

5.1 Highway Base Design

Resilient modulus test results were used to estimate the thickness of the base layer in a pavement

by following the procedures defined in the AASHTO Guide (1993). The 50 million ESAL value

was assumed for this analysis. The overall standard deviation (So) and reliability (ZR) were

assumed to be 0.35 and 95%, respectively. Structural numbers (SN) were back-calculated using

the following equation:

(6)

where ∆PSI is design serviceability loss and MR is the roadbed material effective resilient

modulus. The values were selected as 5000Psi, based on Huang (1993). An asphalt layer

thickness of 8 inches (203.2 mm) was selected. The resilient modulus of asphalt was assumed to

be 430000Psi (2965 Mpa), which corresponded to a layer coefficient of a1 = 0.44 according to

AASHTO Guide (1993). A resilient modulus of 15000lb (103 Mpa) (corresponding to a structural

coefficient of a3 = 0.08) and a thickness of 6 inch (152.4 mm) (D3) were assumed for the subbase

layer. The laboratory-based SMR values vary between 17000 psi (120 Mpa) and 30500 psi (210

Mpa) which correspond to a layer coefficient (a2) of 0.08-0.14 according to AASHTO pavement

design guidelines (1993). SMr of 30000lb (206.84 Mpa) and a2 of 0.12, the two values commonly

used by SHA in absence of measurement, fall within this range. Finally, the base thicknesses were

calculated using the following formula:

07.8log32.2110944.0

5.12.4log20.01log36.9log 1019.5

1010018

RR M

SN

PSISNSZW

47

m2a

mD3

a1D1aSN

2D2

33 (7)

where m2 and m3 are drainage modification factors for base and subbase layer, respectively, and

were chosen as 1.2, 1.0, 0.8, 0.6 for excellent, good, fair, and poor drainage conditions,

respectively, within the pavement system (Huang 1993). D1, D2, and D3 are the layer thicknesses of

asphalt layer, base layer, and subbase layer, respectively.

It can be concluded from Table 6 that an increase in the base layer coefficient yields a

decrease in required thickness of base layer while all other factors are kept constant. On the other

hand, the decrease in drainage modification factor increases the required thickness of the base

course. The effects of layer coefficient and drainage modification factor of GAB on the required

design thickness are also reflected in Figure 18.

5.2. Effect of Hydraulic Conductivity on Highway Base Design

Federal Highway Administration (FHWA) software DRIP (Drainage Requirement in Pavements)

was used to evaluate the effect of hydraulic conductivity on drainage time and minimum required

thickness of highway base layers. For the purpose of analysis, a typical cross section of highway

having width (W) of 24 ft (two lanes, each 12 ft wide) was selected. The longitudinal slope (S)

and cross slope (Sx) were considered as 2%, and the resultant length of flow path (LR =

W*[1+(S/Sx)2]1/2) was calculated.

The largest source of water is the rain water that enters the pavement surface through

cracks and joints in the surface. Two methods have been used to determine surface infiltration of

water: the infiltration ratio method (Cedergren et al. 1973) and the crack infiltration method

48

(Ridgeway 1976). The infiltration ratio method is highly empirical and depends on both the

infiltration ratio and rainfall rate. The crack infiltration method, on the other hand, is based on the

Table 6. Effect of change in layer coefficient and drainage modification factor on the required base thickness in pavement design.

a2 m3 = m2 D2 (in) Cost /mile ($)

0.08

0.6 24.8 594173

0.8 17.1 409742

1 12.5 299065

1.2 9.4 225311

0.10

0.6 19.9 475300

0.8 13.7 327794

1 10.0 239252

1.2 7.5 180192

0.12

0.6 16.6 396084

0.8 11.4 273161

1 8.3 199408

1.2 6.3 150239

0.14

0.6 14.2 339474

0.8 9.8 234165

1 7.1 170868

1.2 5.4 128763

Notes: a2: base layer coefficient, m2 : base layer drainage modification factor, m3: subbase layer drainage modification factor, D2: required base thickness

49

5

10

15

20

25

0.50.60.70.80.911.11.21.3

a2 = 0.08

a2 = 0.10

a2 = 0.12

a2 = 0.14

Re

qu

ired

bas

e th

ick

ne

ss

(in

)

Base course drainage modification factor (m2)

FIGURE 18: Variation of required base thickness with the change in layer coefficient and drainage

modification factor.

50

results of infiltration tests, and was preferred in the current analysis. The equation to compute the

infiltration rate for intact pavement is as follows:

ps

ccci k

WC

W

W

NIq

(8)

where qi is rate of pavement infiltration (ft3/day/ft2), Ic is the crack infiltration rate, (ft3/day/ft), Nc

is number of longitudinal cracks. Ic and Nc were assumed as 2.4 ft3/ft/day, and 3, respectively. The

length of contributing transverse joints or cracks (Wc, ft), the width of base (W, ft), and the

spacing of contributing transverse joints or cracks (Cs, ft) were 24 ft, 26 ft , and 24 ft respectively.

kp is pavement hydraulic conductivity (ft/day) and a value of 0.167 ft/day was assumed per Kutay

et al. (2007).

Two approaches were used to evaluate the drainage ability of the GAB layers: Depth-to-

flow design approach and time-to-drain approach. In the first approach, the concept is that the

steady flow capacity of base layer should be equal to or greater than the inflow of rainfall. Moulton

(1980) developed an equation which presents that required base thickness (H) as a function of

GAB hydraulic conductivity (k), slope (S) of highway, length of drainage (LR), and rate of

pavement infiltration (qi). The equations for depth-to- flow are:

2/4

1tan41

22

Sk

q

S

Sk

q

SRL

k

qH

ii

i

if (S2 – 4qi/k) < 0 (9)

kiqS

S

i

ii

kqSS

kqSSRL

k

qH

/422

2

2

/4

/41

if (S2 – 4qi/k) > 0 (10)

51

1

1

RL

k

qH i

if (S2 – 4qi/k) = 0 (11)

where S and LR were assumed as 0.0283 and 36.8 ft respectively. The highway geometry is

beyond the scope of this work, thus a sensitivity analysis was conducted with respect to hydraulic

conductivity (k) only. The laboratory quarry GAB hydraulic conductivities listed in Table 4 were

used in the analysis. The results shown in Figure 19 indicate that the required base thickness is

more influenced from GAB permeability’s at k < 300ft/day.

0

10

20

30

40

50

60

70

80

0 500 1000 1500 2000 2500 3000 3500 4000

Req

uir

ed b

ase

th

ickn

ess

(in

)

Hydraulic conductivity (ft/day)

FIGURE 19: Variation in required base thickness with base course hydraulic conductivity

52

The second approach for design of the GAB layers includes a series of calculations for the

time to drain 50% of the infiltrating water. The AASHTO pavement design guideline (1993)

categorizes the base layer as excellent, good, fair, and poor based on time for 50% drainage. The

following equations developed by Casagrande and Shannon (1952) and Barber and Sawyer (1952)

and embedded in the DRIP software were used to calculate the time to drain a specified percentage

of the infiltrating water:

Casagrande and Shannon (1952)

kH

Ln

SU

USSS

S

SSS

St e

2

1

111

1

12113/1

1122

122ln

1ln

4.02.1

if U > 0.5 (12)

kH

Ln

S

USSUS

St e

2

1

12113/1

1

2ln2

4.02.1

if U ≤ 0.5 (13)

Barber and Sawyer (1952)

kH

Ln

SU

USSS

SSSt e

2

1

111

1

211 4.21

2.1log15.1

4.21log48.05.0

if 0.5 U 1.0 (14)

kH

Ln

S

USUSt e

2

1

21

8.41log48.0

if 0 U 0.5 (15)

where t is time (hours) for percent drainage, U, to be reached, S1 is dimensionless slope factor (=

H/LS). L and ne are width and effective porosity of the GAB layers and were taken as 24ft (7.3 m)

and 20- 70% of total porosity, respectively.

53

A series of analysis was performed to gage the influence of U, H, and k on time-to-drain.

U and H varied between 0 and 98%, and 2 inch to 24 inch (5 and 60 cm), respectively. A unit

weight of 140 Pcf (2242.5 kg/m3) and specific gravity of 2.70 for GAB were used. The water in

the voids cannot be drained by gravity flow due to capillary action present in the soil matrix, thus,

effective porosities were assumed to be 20-70% of the total porosities (Moulton 1980) and utilized

in the analysis. Four different gradations of A- and B-GAB materials, with fines content of 2, 4,

8, and 14%, along with the lower and upper SHA gradation limits were used in the drainage

calculations. A-GAB hydraulic conductivities ranging from 88ft/day to 18ft/day (6.4 x 10-3 to 3.12

x 10-2 cm/s) that correspond to 2-16% fines content were utilized. The corresponding T- GAB

hydraulic conductivities ranged from 10ft/day to 0.2ft/day (3.67 x 10-3 to 7.32 x 10-5 cm/s).

The results are shown in Figures 20-23. The time-to-drain does not change significantly up

to 50% drainage, and an exponential increase in drainage time exists for 50%<U<98% (Figures 20

and 22). Moreover, when the base thickness of R- GAB was increased from 2 inches to 24 inches

(6 to 60 cm), 57% and 48% decreases in drainage time were observed based on Barber and Sawyer

(1952) and Casagrande and Shannon (1952) methods, respectively (Figure 21). The corresponding

decreases in drainage time for T- GAB were 45% and 54%, respectively (Figure 23).

The driving factor for time-to-drain of a highway base is the GAB hydraulic conductivity.

The required base layer thickness (Moulton method 1980) with respect to hydraulic conductivity

values obtained at 2-14% fines are shown for R- GAB in Figure 24 and Table 7. At a specific base

thickness of 12in (0.3m), the time-to-drain (at U=50%) increases three times with change of fines

content from 2 to 14% (24h to 75h, changing the corresponding AASHTO drainage quality

classification from Good to Fair (Table 7). The corresponding increase in time-

54

0

200

400

600

800

1000

1200

0 20 40 60 80 100

2% fines4% fines8% fines14% finesSHA upper limitSHA lower limit

Tim

e to

dra

in (

ho

urs

)

Drainage (%)

Barber and Sawyer Method

(a)

(H=0.3 m)

A-GAB

0

200

400

600

800

1000

1200

0 20 40 60 80 100


Tim

e to

dra

in (

ho

urs

)

Drainage (%)

Casagrande and Shannon Method

(b)

(H=0.3 m)

A-GAB

FIGURE 20: Variation in time to drain with percent drainage and fines content for A GAB: (a)

Barber and Sawyer Method, and (b) Casagrande and Shannon Method. H= 0.3 m was used throughout the analysis

55

0

50

100

150

200

0 5 10 15 20 25


Tim

e t

o d

rain

at

U=

50

% (

ho

urs

)

Base thickness (in)


(a)

A-GAB

0

50

100

150

200

250

0 5 10 15 20 25


Tim

e to

dra

in a

t U

=5

0%

(h

ou

rs)

Base thickness (in)


(b)

A-GAB

FIGURE 21: Variation in time to drain with base thickness and fines content for A GAB: (a)

Barber and Sawyer Method, and (b) Casagrande and Shannon Method.

56

0

1000

2000

3000

4000

5000

6000

7000

0 20 40 60 80 100


Tim

e to

dra

in (

ho

urs

)

Drainage (%)


(a)

(H=0.3 m)

B-GAB

0

1000

2000

3000

4000

5000

6000

7000

0 20 40 60 80 100


Tim

e to

dra

in (

ho

urs

)

Drainage (%)


(b)

(H=0.3 m)

B-GAB

FIGURE 22: Variation in time to drain with percent drainage and fines content for B GAB: (a)

Barber and Sawyer Method, and (b) Casagrande and Shannon Method. H= 0.3 m was used throughout the analysis

57

0

1000

2000

3000

4000

5000

6000

7000

0 5 10 15 20 25


Tim

e t

o d

rain

at

U=

50

% (

ho

urs

)

Base thickness (in)


(a)

B-GAB

0

1000

2000

3000

4000

5000

6000

7000

0 5 10 15 20 25


Tim

e to

dra

in a

t U

=50

% (

ho

urs

)

Base thickness (in)


(b)

B-GAB

FIGURE 23: Variation in time to drain with base thickness and fines content for B GAB: (a) Barber and Sawyer Method, and (b) Casagrande and Shannon Method

58

20

30

40

50

60

70

80

20

25

30

35

40

45

50

55

10 20 30 40 50 60 70 80 90

Time to drain

Base thickness

y = 525.97 * x^(-0.67727) R2= 0.99818

y = 302.87 * x^(-0.59473) R2= 0.99934 T

ime

to

dra

in a

t U

=50

% (

ho

urs

)R

eq

uire

d b

as

e th

ickn

ess

(in)


14% fines

8% fines

4% fines

2% fines

FIGURE 24: Influence of hydraulic conductivity on required base thickness (Moulton method) and time-to-drain of highway base layers constructed with (a) A-Quarry, and (b) B-Quarry GABs.

A-GAB

59

Table 7. Effect of fines content and GAB type on required base thickness and time to drain.

Material Gradation Hydraulic

conductivity ft/day (cm/s)

Time to drain at U=50% (hours)

AASHTO classification

for quality of drainage

R-Q

uarr

y

2% FC 88 (3.12 x 10-2) 24 Good

4% FC 54 (1.92 x 10-2) 36 Fair

8% FC 29 (1.04 x 10-2) 52 Fair

14% FC 18 (6.33 x 10-3) 75 Fair

Quarry 77 (2.73 x 10-2) 48 Fair

T-Q

uarr

y

2% FC 10 (3.67 x 10-3) 218 Poor

4% FC 8 (2.71 x 10-3) 263 Poor

8% FC 1 (4.22 x 10-4) 1262 Poor

14% FC 0.001 (7 x 10-5) 3768 Poor

Quarry 19 (6.57 x 10-3) 77 Fair

E-GAB Quarry 11 (3.92 x 10-3) 138 Fair

G-GAB Quarry 36 (1.28 x 10-2) 35 Fair F-GAB Quarry 4 (1.48 x 10-3) 430 Poor C-GAB Quarry 2 (5.66 x 10-4) 1121 Poor

D-GAB Quarry 42 (1.48 x 10-2) 33 Fair SHA lower limit 90 (3.19 x 10-2) 29 Good

SHA upper limit 11 (3.84 x 10-3) 140 Fair

60

to-drain for T-GAB was from 218 hours to 3768 hours (Table 7). A 17 times increase in time-to-

drain indicated that material was clogged by fines. It can also be seen from Figures 11 and 12 that

T- GAB stays out of the SHA gradation limits and experience unacceptable hydraulic

conductivities when the fines content was greater than 6%.

Figure 25 and 26 present the variation in time-to-drain with percent drainage and base

thickness for all GABs. The F and C GABs materials took long time to drain as compared to

others due to their relatively lower hydraulic conductivities (Table 4). The effect of hydraulic

conductivity on drainage performance and required base thickness can clearly be seen in Figure

27. The GAB materials with lower hydraulic conductivities yielded higher time-to-drain at

U=50% and required large base thicknesses for construction.

61

0

200

400

600

800

1000

1200

0 20 40 60 80 100

ABEGFCDSpec.Upper LimitSpec.Lower Limit

Tim

e to

dra

in (

ho

urs

)

Drainage (%)


(a)

(H=0.3 m)

0

200

400

600

800

1000

1200

0 20 40 60 80 100


Tim

e to

dra

in (

ho

urs

)

Drainage (%)


(b)

(H=0.3 m)

FIGURE 25: Variation in time to drain with percent drainage and GAB type: (a) Barber and

Sawyer Method, and (b) Casagrande and Shannon Method. H= 0.3 m was used throughout the analysis

62

0

500

1000

1500

0 5 10 15 20 25


Tim

e t

o d

rain

at

U=

50%

(h

ou

rs)

Base thickness (in)


(a)

0

500

1000

1500

2000

0 5 10 15 20 25


Tim

e t

o d

rain

at

U=

50%

(h

ou

rs)

Base thickness (in)

Casagrande and Shannon

Method

(b)

FIGURE 26: Variation in time to drain with base thickness and GAB type: (a) Barber and Sawyer Method, and (b) Casagrande and Shannon Method.

63

0

50

100

150

200

250

0 10 20 30 40 50 60 70 80

y = 341.47 * x^(-0.58672) R2= 0.9952

Re

qu

ire

d b

ase

th

ick

ne

ss

(in

)


A-GAB

D-GAB

B-GAB

E-GAB

F-GAB

C-GAB

G-GAB

0

200

400

600

800

1000

1200

0 10 20 30 40 50 60 70 80

y = 1494.4 * x (-0.9457) R2= 0.99753

Tim

e to

dra

in a

t U

= 5

0%

(h

ou

rs)


A-GABD-GABB-GAB

E-GAB

F-GAB

C-GAB

G-GAB

Thickness of base (H) = 0.3m

FIGURE 27: Effect of GAB hydraulic conductivity on (a) required base thickness, and (b) time to

drain at 50% drainage.

64

5.3. Cost Calculations

A simple cost analysis was performed on all GAB materials. The design thicknesses of the base

layers (Table 6) were calculated using Equation 6 by assuming a pavement structural number (SN)

of 5 based on 50 million ESAL value, a layer coefficient (a1) of 0.44 for the asphalt layer, and a

layer coefficient (a3) of 0.08 for the subbase layer. The layer coefficient of base layer (a2) was

varied between 0.08 and 0.14 based on laboratory SMR of the GAB materials, and the drainage

coefficient of both base and subbase (m1 and m2) were assumed to remain in a range of 0.6-1.2.

The average unit price of the GAB material was considered to be $80/m3 ($10/ yd3 per 6-in lift

thickness) following the 2013 price index table issued by Maryland SHA. The listed unit price of a

GAB material includes material, hauling, transportation and laying costs only.

Lane widths in the United States can range from 11.5 ft (low volume roads) to 15 ft

(highway ramps), and a typical design lane width of 12ft (3.65 m) was selected for the cost

analysis in the current study. A two-lane roadway was considered. The cost analysis summarized

in Table 6 indicates that the GAB cost decreases with increasing drainage modification factor or

layer coefficient, and vice versa. It can be seen from Figure 28 that the cost decreases 62% with

the increase in quality of drainage from poor to excellent or time-to-drain from 10 to 0.08 days. A

42% cost decrease is noticeable with a layer coefficient increase of 0.08 to 0.14. The construction

cost of 1-mile highway varies from $128763 to $594173, which indicates that proper selection of a

highway base layer coefficient and drainage modification factor has a significant impact on the

construction costs.

65

0.5

1

1.5

2

2.5

3

3.5

4

10 7 1 0.08

Poor Fair Good Excellent

a2 =0.08

a2=0.10

a2 = 0.12

a2=0.14

Time to drain water from the base layer (days)

Quality of Drainage

Co

st

of

GA

B la

yer

X 1

05 $

FIGURE 28: Effect of time to drain and quality of drainage on the cost of GAB layer.

6. CONCLUSIONS

The structural stability and drainability of pavement structures depend on the mechanical and

hydraulic characteristics of graded aggregate base (GAB) materials. A research study was

conducted to evaluate the drainage and mechanical properties of GAB materials utilized in

Maryland highways. In addition to seven GAB materials, two recycled concrete GAB materials

and their selected mixtures were studied. The observations are summarized as follows:

66

1) The GAB resilient modulus, Mr. increased when fines content was varied between 2 and 8%

and, started decreasing with further fines addition. SMr was maximized when the fines

content was ~8% and gravel-to-sand ratio was 1.6-1.7. A minimum gravel content of 70%

(>4.75 mm) by weight and a maximum fines content of 8% by weight should be specified

for highway base construction with the GABs tested. This can be controlled by avoiding

segregation of the GABs and their proper mixing by pig mill at the construction site.

2) The GAB resilient modulus generally decreased with moisture addition above OMC during

compaction. SMr values at OMC-2% were higher than those at OMC, with few exceptions.

On the other hand, the permanent deformations (i.e., a measure of structural rutting) were

doubled with 2% increase in moisture contents from the OMCs; however, no significant

change in permanent deformations occurred at OMC-2%. The findings suggest that the

field compaction mositure content should be as close to OMC as possible.

3) The RCAs experienced 0.98 to 2.1 times increase in SMR with increasing freeze-thaw cycles

due to ongoing hydration process during freezing and thawing. The SMR of RCAs

mixtures were lower than the ones for 100%RCA and 100%GAB materials, with few

exceptions. Similarly, permanent deformations of the mixtures were generally higher than

those obtained for pure GAB or RCA.

4) An addition of 4-6% fines over the SHA specification limit of 8% resulted in 2-5 times

decrease in the laboratory-based GAB hydraulic conductivities and led to an increase in

time for 50% completion of the drainage from the highway base (from 50 hr to 75 hr). The

required base thickness based on Moulton method (1979) was also increased 2.5 times as a

result of the reduction in GAB hydraulic conductivity. The laboratory and field hydraulic

67

conductivities were generally comparable and ratio of the laboratory-to-field hydraulic

conductivity was 0.6-3.5.

5) The correlation between the mean laboratory and field stiffness/modulus values were fair to

acceptable (R2=0.65 to 0.9); however, further research is required to improve the accuracy

of correlation between the laboratory and field stiffness/modulus values.

6) If percentage of fine materials is not carefully controlled during construction process, the

base layer built with GAB materials may experience clogging which may, in turn, initiate

the deterioration of the upper pavement layer (asphalt layer). Considering the hydraulic

conductivity, resilient modulus and permanent deformation data of the current study, the

fines content should be limited to 8% and gravel-to-sand ratio should be kept between 1.6

and 1.7.

7) A simple cost analysis suggested that improper selection of layer coefficient and drainage

modification factor for the base layer may lead to immature failure or uneconomical design. In

cases where clogging of the base is of concern, a drainage analysis should be conducted in

addition to geomechanical testing for cost-based selection of the layer coefficient.

IMPLEMENTATION PLAN AND GUIDELINES FOR PAVEMENT DESIGN.

1. Based on the resilient moduli (MR) of graded aggregate base (GAB) materials of seven

quarries located in different districts of Maryland, the corresponding layer coefficient can be

used in the pavement design for new construction.

2. The variation in moisture content more than 2% above or below the optimum moisture

content could cause significant change in the resilient modulus of the GAB material. The

field moisture content of a GAB material should be controlled well during compaction

process.

68

3. The resilient modulus of GAB is stress dependent provided density, gradation and moisture

content are kept constant. Thus, the representative design resilient modulus of GAB material

should be selected based on the stress level in the GAB layer, thickness of pavement

structure and loading conditions.

4. Fines content greater than 8% by weight could cause the clogging of a GAB material,

resulting in immature failure due to development of excessive pore water pressure in the

GAB layer.

5. The drainage of GAB depends on a number of factors: compaction level, gradation, void

ratio, porosity, particle shape, hydraulic gradient, and road geometry. Thus, it is important

to run at least one test on the JMF conditions and road geometry to analysis the drainage

ability of a specific GAB material.

6. The resilient moduli results are calibrated with a model given in AASHTO Pavement

Design Guide. By using the k1, k2, k3 coefficients, the resilient moduli of seven quarries can

be estimated at different stress levels. On the other hand, the drainage modification factors

being used in pavement design are also calculated based on the time to drain for 50%

drainage by DRIP software. The drainage modification factors can be used in pavement

design.

7. The structural coefficient of all GAB materials varies in between 0.08 to 0.14 based on their

resilient moduli at maximum optimum density, moisture content and 208 kPa bulk stress.

8. The geometry of road, fines content, gradation, porosity and hydraulic conductivity are the

factors which affect the drainage capacity of GAB material. To estimate the drainage

capacity of a GAB material, the above mentioned factors should be considered and DRIP

69

software should be run to find the time to drain at 50% drainage to define a drainage

modification factor.

9. SHA should consider including resilient modulus and hydraulic conductivity tests in the

aggregate bulletin to develop the database for other quarries that are not tested in this

research study.

10. The layer coefficient of a GAB material used in pavement design should be based on the

laboratory resilient modulus at OMC, Max density, JMF gradation and given stress level.

The drainage modification factor should be based on the laboratory hydraulic conductivity

value and time to drain at 50% drainage, geometry of road. At the time of preparation of

JMF of GAB material, the resilient modulus and hydraulic conductivity properties can be

optimized for a specific GAB material by running few tests in the laboratory.

11. There are three levels of inputs in the MEPDG design for GAB material. For level # 1 input,

we have to run laboratory testing on GAB material to obtain the resilient modulus and

hydraulic conductivity. For Level # 2, correlation between index properties and resilient

modulus and hydraulic conductivity are available, which can be used for level 2 input. Level

# 3 inputs are default values of GAB materials. The selection of level is based on the type

of the highway studied.

70

REFERENCES AASHTO Guide (1993). Guide for design of pavement structures. American Association of

State Highway and Transportation Officials, Washington D.C. AASHTO Specification (2010). Standard specification for transportation materials and methods

of sampling and testing. American Association of State Highway and Transportation Officials, Washington D.C.

ASTM. Specification (2010). Annual Books of ASTM Standards, Road and Paving Materials. American Standards of Testing Materials.

Abhijit.P., Patil,.J. (2011). Effects of bad drainage on roads. Civil and Environmental Research, IISTE, Vol 1, No.1. India

Barber, E.S and Sawyer, C.L. (1952). Highway subdrainage: Proceedings of Highway Research Board. pp 643-666.

Bozyurt, O., Keene,A., Tinjum,J.M., Edil,T.B. (2012). Freez-thaw effects on stiffness of unbound recycled road base. Transportation Research Board Annual Meeting. Washington D.C.

Bennert,T., Maher, A. (2005). The development of a performance specification for granular base and subbase material. Federal Highway Authority, FHWA-NJ-2005-003. New Jersey.

Cetin, B., Aydilek, A.H., Guney. Y. (2010). Stabilization of recycled base materials with high carbon fly ash. Resources, Conservation and Recycling 54. pp 878-892

Casagrande, A., Shannon, W.L. (1952). Base course drainage for airport pavements. Proceedings of American Society of Civil Engineers. Vol.77, pp.792-814.

Cedergren, H.R., Arman, J.A., O'Brien, K.H. (1973). Development of guidelines for the design of subsurface drainage systems for highway pavement structural sections. Federal Highway Administration. FHWA-RD-73-14.

Christopher, B. R., and McGuffey, V.C. (1997). NCHRP synthesis of highway practice no.239: Pavement subsurface drainage systems. National Research Council, Washington, D.C.

Dawson, A. R., Thom, N. H., and Paute, J. L. (1996). Mechanical characteristics of unbound granular materials as a function of condition. Flexible Pavements, Proc., Eur. Symp. Rotterdam, Netherlands, pp 35-44.

Dawson, A.R. (2009). Introduction. in: Water in Road Structure. Ch. 1. Springer. p 436 Diefenderfer.P., Abhijit.P., Patil,.J. (2001). Six adverse effects of bad drainage on roads. Civil

and Environmental Research, Vol 1, No.1. India Erlingsson, S., Baltzer, S., Baena, J. and Bjarnason, G (2009). Measurement techniques for

water: water in road structures. Ch. 3. Springer. p 436. Haynes, J. G., and Yoder, E. J. (1963). Effects of repeated loading on gravel and crushed stone

base course materials used in the aasho road test. Highway Research Record 39. Hicks, R. G. (1970). Factors influencing the resilient properties of granular materials. PhD

Thesis. Berkeley: University of California. Hicks, R. G. and Monismith, C. L. (1971). Factors influencing the resilient properties of

granular materials. Highway Research Record 345. pp 15-31. Jorenby, B. N., and Hicks, R. G. (1986). Base course contamination limits. Transportation

Research Record 1095. pp 86-101.

71

Kamal, M. A., Dawson, A. R., Farouki, O. T., Hughes, D. A. B. (1993). Field and laboratory evaluation of the mechanical behaviour of unbound granular materials in pavements. Transportation Research Record 1406. pp. 88-97.

Kazmee, H., Tutumluer, E., Mishra,D. (2012). Effects of material blending on strength, modulus and deformation characteristics of recycled concrete aggregates. Transportation Research Board Meeting. Washington D.C

Khogali, W. E., and Mohammed, E. H. (2004). Novel approach for characterization of unbound materials. Transportation Research Record 1874. pp 38-44.

Kolisoja, P. (1997). Resilient deformation characteristics of granular materials. PhD Thesis. University of Technology, Tampere.

Kutay,M.E., Aydilek, A.H., Masad,E., Harman,T. (2007). Computational and experimental evaluation of hydraulic conductivity anisotropy in hot-mix asphalt. International Journal of Pavement Engineering, Vol. 8, No. 1, pp 29–43.

Moulton, L.K. (1980). Highway subdrainage design. Federal Highway Administration, FHWA-TS-80-224. Washington D.C

NCHRP 1-28A. (2003). Harmonized test methods for laboratory determination of resilient modulus for flexible pavement design. Asphalt concrete material. Vol. II

NCHRP 626. (2009). NDT technology for quality assurance of HMA pavement construction. Transportation Research Board.

NCHRP 10-65 (2006). Nondestructive Testing technology for quality control and acceptance of flexible pavement Construction. Transportation Research Board.

Poon, C.S., Shui, Z.H., Lam, L., Fok.H. (2004). Influence of moisture states of natural and

recycled aggregates on the slump and compressive strength of concrete. Cement and Concrete Research. Vol.34, pp 31-36.

Rada, G., and Witczak, M. W. (1981). Comprehensive evaluation of laboratory resilient moduli results for granular material. Transportation Research Record. 810. pp 23-33. Washington D.C.

Ridgeway, H.H. (1976). Infiltration of water through the pavement surface. Transportation Research Record 616,TRB, National Research Council, Washington D.C. pp 98-100.

Robinson, R.G. (1974). Measurement of the elastic properties of granular materials using a resonance method. TRRL Supplementary Report.No. 111UC,

Smith, W. S., and Nair, K. (1973). Development of procedures for characterization of untreated granular base coarse and asphalt-treated base course materials. Federal Highway Administration, FHWA-RD-74-61.Washington D.C.

Sweere, G. T. H. (1990). Unbound granular basis for roads, PhD Thesis. University of Delft, The Netherlands.

Thom, N. H., and Brown, S. F. (1987). Effect of moisture on the structural performance of a crushed-limestone road base. Transportaton Research Record 1121. pp 50-56 Washington D.C..

Siswosoebrotho,B.I., Widodo.P, Augusta.E. (2005) .The influence of fines content and plasticity on the strength and permeability of aggregate for base course material. Eastern Asia Society for Transportation Studies. Indonesia, Vol 5, pp 845-856.

Uzan, J. (1985). Characterization of granular material. Transportation Research Record 1022, pp 52-59. Washington D.C.

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Uthus, L. (2007). Deformation properties of unbound granular aggregates, PhD Thesis. Department of civil and transportation engineering, Norwegian University of Science and Technology.

Vuong, B. (1992). Influence of density and moisture content on dynamic stress-strain behavior of a low plasticity crushed rock. Road and Transportation Research, TR 1(2). pp 88-100.

White, D., Bergeson, K., and Jahren, C. (2002). Embankment quality: phase III. Department of Civil and Construction Engineering, Iowa State University, final report, Iowa DOT Project TR-401, CTRE Project 97-08,

Witczak, M.W. and Uzan, J. (1988). The universal airport pavement design system. Report I of IV. University of Maryland.

Xiao,Y., Tutumluer, E., Qian, Y. (2012). Gradation effects influencing mechanical properties of aggregate base and granular subbase in Minnesota. Transportaton Research Board Meeting. Washington D.C.

73

APPENDIX

74

APPENDIX A

COMPACTION AND RESILIENT MODULUS TESTING

75

0

20

40

60

80

100

0.010.1110100

Per

cent

Fin

er

Particle Size, mm

Virgin

Vibratory Comp.

Impact Comp.

(a)

0

20

40

60

80

100

0.010.1110100

Per

cent

Fin

er

Particle Size, mm

VirginVibratory Comp.Impact Comp.

(b)

0

20

40

60

80

100

0.010.1110100

Per

cent

Fin

er

Particle Size, mm


(c)

0

20

40

60

80

100

0.010.1110100

Per

cent

Fin

er

Particle Size, mm


(d)

FIGURE A.1: Particle degradation of GAB materials due to vibratory and impact compaction: (a) A-GAB (b) B-GAB (c) G-GAB (d) CH-GAB

76

Table A.1 AASHTO T 307-99 resilient modulus testing sequence for base and subbase

materials.

Sequence No.

Confining Pressure,

S3

(Psi)

Maximum Axial

Stress, Smax

(Psi)

Cyclic Stress, Scyclic

(Psi)

Constant Stress,

0.1 Smax (Psi)

Cycles

0 14.993 14.993 13.4995 1.4935 500 1 3.0015 3.0015 2.697 0.3045 100 2 3.0015 6.003 5.4085 0.5945 100 3 3.0015 9.0045 8.1055 0.899 100 4 5.0025 5.0025 4.495 0.5075 100 5 5.0025 9.9905 8.99 1.0005 100 6 5.0025 14.993 13.4995 1.4935 100 7 9.9905 9.9905 8.99 1.0005 100 8 9.9905 19.9955 17.9945 2.001 100 9 9.9905 29.986 26.9845 3.0015 100 10 14.993 9.9905 8.99 1.0005 100 11 14.993 14.993 13.4995 1.4935 100 12 14.993 29.986 26.9845 3.0015 100 13 19.9955 14.993 13.4995 1.4935 100 14 19.9955 19.9955 17.9945 2.001 100 15 19.9955 39.991 35.989 4.002 100

77

STEP-BY-STEP RESILIENT MODULUS TEST PROCEDURE

1) Turn on Geocomp Load Trac II

2) Turn the air pressure pump on

3) Measure the specimen height and diameter

4) Place the porous stone on bottom plate

5) Place the filter paper on bottom porous stone

6) Place the specimen on bottom plate

7) Place the filter paper on top of the specimen

8) Place the porous stone on filter paper

9) Place the top plate on top of the specimen

10) Place rubber membrane over specimen using a mold

11) Place two O- rings on both bottom and top of the plates to hold the membrane in place

12) Plug the drainage tubes on top plate.

13) Place the cell on bottom cap

14) Place cover plate, it should not be tight

15) Place LVDT on top of chamber

16) Screw cover plate with three rods carefully

17) Plug air supply hose into cell

18) Log into PC and open the Resilient modulus RM version 5.0 software

19) Input Specimen height, diameter, and weight.

20) Input the loading and pressure data which is designed for base and subbase test protocol

21) Click on the load calibration menu and check the applied load with the load data that you

entered

22) Click run test and save the file.

78

APPENDIX B

PICTURES OF EQUIPMENT USED IN THE CURRENT RESEARCH STUDY

79

FIGURE B.1 (a) Resilient Modulus Testing Equipment (b) Vibratory compactor.

(b)

(a)

80

FIGURE B-2: Bubble tube constant head permeameter

81

FIGURE B.3: Nuclear density gauge for compaction and moisture content testing.

82

FIGURE B.4: Light weight deflectometer

83

FIGURE B.5: Geogauge used in field testing (After Humboldt Mfg.Co)

84

D

SealCasing

ScaleStandpipe

H

( ) S

Zw

Dc

FIGURE B.6: Field hydraulic conductivity test on GAB. (ASTM D6391--Stage 1)

85

APPENDIX C

FIELD TEST RESULTS

86

Table C.1 Field Tests Results.

Quarry Name

Geogauge LWD Nuclear Density GaugeYoung's Modulus

(Psi)

Stiffness (lb./in)

Modulus (Psi)

Density (Pcf)

Moist (%)

A-V1 32407.0 126536.7 10213.0 149.9 4.7 A-V2 32602.7 148006.8 12326.1 139.3 6.4 A-V3 33744.1 153146.0 11822.8 142.2 7.1 A-V4 27448.4 124538.2 9843.2 147.4 4.7 A-V5 26562.2 120541.1 7276.2 145.9 4.3 A-V6 30795.7 139727.1 10165.2 142.4 4

A-VII1 31444.0 142696.4 12910.6 152.5 0.2 A-VII2 30807.3 139841.3 15995.4 155.8 0.7

A-VII3 33687.6 152860.4 12759.7 148.5 1 B-1 201111.1 25251.2 137.5 4.4 B-2 29596.3 143952.6 19215.0 137.1 4.8 B-3 191803.6 18458.0 146.4 3.6 B-4 46057.2 209048.2 31333.7 151.2 2.9 B-5 41169.7 186835.8 24459.3 151.9 2.9 D-1 11051.3 50135.0 8271.1 156.3 6.2 D-2 13883.7 148463.6 3792.5 153.7 5.8 D-3 20182.4 91590.6 5870.8 152.1 6.7 D-4 9174.6 41626.9 3023.9 155.9 7.1 D-5 28241.7 128192.6 12445.0 155.1 6.3 D-6 21313.6 96729.8 3276.2 154.3 6.7 D-7 6285.6 28550.7 2319.0 152.6 6.8

D-So1 22282.4 101126.6 12311.6 152.8 6.2 D-So2 21413.7 97186.6 6007.1 156.7 5.5 D-So3 21686.3 98442.8 8694.5 151.9 5.7 D-So4 12909.1 58586.0 4546.7 153.5 5.2 D-So5 27622.4 104609.8 9782.3 152.1 6.2 D-So6 22645.0 102782.5 6690.2 159.2 5.4 D-So7 16527.6 75031.2 2517.7 152.6 6.8 D-So8 16624.8 75488.1 3002.1 152.6 4.5 D-So9 27317.9 123339.0 16762.6 154.9 7.8 D-So10 30159.0 136872.1 17181.7 151.5 7.1 D-So11 17780.7 80684.3 12548.0 151.0 6.5 D-So12 17290.5 78514.4 8482.8 152.1 6.3 B-695-1 20037.3 90905.4 11016.5 149.0 5.1 B-695-2 19135.3 86851.2 10392.8 142.6 4.6

87

B-695-3A 20914.8 94902.5 5873.7 146.6 4.1

B-695-3B 25209.1 114431.2 8021.6 142.8 5.2 B-695-4 20862.6 94674.1 7550.3 144.6 4.9

G-1 19908.3 90391.5 8169.5 151.7 5.2

G-2 23193.2 105295.0 8758.4 151.4 6.6 G- 3 24286.7 110262.8 7915.7 150.6 6.8

G-4 29448.3 133674.4 12970.0 147.9 6

G-5 24575.3 111576.1 7442.9 156.9 5.7 G-6 25918.3 117628.9 9962.1 152.4 5.5

C-1 18675.5 84738.5 7728.6

C-2 14765.5 67037.0 7411.0

C-3 28794.3 131732.9 10893.2

C-4 15870.6 72062.0 5553.2

C-5 19064.2 86508.6 4321.9

C-6 19847.4 90106.0 6897.6

88

APPENDIX D

ABBREVIATIONS AND ACRONYMS

89

ϒd: Maximum dry density

Imp: Impact compactor

Vib: Vibratory compactor

Gs: Specific gravity

F: Fine contents

C: Coarse contents

LA: Los Angeles abrasion test

MD: Micro deval test

SS: Loss in Sodium Sulfate test

PD: Petrographic Description

MR : Resilient modulus

k1, k2, and k3 : Constants

sotropic confining pressure

d: Deviator stress

pa: Atmospheric pressure

SMR: Summary resilient modulus

Eo: Surface modulus

f: Plate rigidity factor

v: Poisson’s ratio

σo: Maximum contact stress

a: Plate radius (in)

do: Maximum deflection (in).

P: Load

δ: Deflection,

R: Radius of the contact ring

E: Shear modulus and

CBR: California bearing ratio

OMC: Optimum moisture content

ϵplastic: Plastic strain of specimens

NA: Not analyzed.

90

A: RCA from Plant A

B: RCA from Plant B

FC: Fines content

G/S: Gravel-to-sand ratio,

k: Hydraulic conductivity

a2: Base layer coefficient,

m2: Base layer drainage modification factor

m3: Subbase layer drainage modification factor

D2: Required base thickness

∆PSI : Design serviceability loss

So: Overall standard deviation

ZR: Reliability

SN = Structural numbers

D1, D2, and D3 : Layer thicknesses of asphalt layer, base layer, and subbase layer

S: Longitudinal slope

Sx : Cross slope

LR: Resultant length of flow path

qi: Rate of pavement infiltration (m3/day/m2),

Ic: Crack infiltration rate, (m3/day/m),

Nc: Number of longitudinal cracks.

kp: Pavement hydraulic

H: Base thickness

t: Time for percent drainage, U, to be reached

S1: Dimensionless slope factor (H/LS).

L: Width of GAB

ne: Effective porosity of the GAB

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